



'''simple docstring'''



from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 						lowerCamelCase_				:
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='codeparrot/codeparrot'     ,					metadata={'help': 'Model name or path of model to be trained.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='./'     ,					metadata={'help': 'Save dir where model repo is cloned and models updates are saved to.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='codeparrot/codeparrot-clean-train'     ,					metadata={'help': 'Name or path of training dataset.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='codeparrot/codeparrot-clean-valid'     ,					metadata={'help': 'Name or path of validation dataset.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(default=2     ,					metadata={'help': 'Batch size for training.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(default=2     ,					metadata={'help': 'Batch size for evaluation.'}      )
						_lowerCAmelCase  : Optional[float]						  =					field(default=0.1     ,					metadata={'help': 'Value of weight decay.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(
						    default=1_0_0_0_0     ,					metadata={'help': 'Size of buffer used to shuffle streaming dataset.'}      )
						_lowerCAmelCase  : Optional[float]						  =					field(default=2E-4     ,					metadata={'help': 'Learning rate fo training.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(default='cosine'     ,					metadata={'help': 'Learning rate.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(
						    default=7_5_0     ,					metadata={'help': 'Number of warmup steps in the learning rate schedule.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(
						    default=1_6     ,					metadata={'help': 'Number of gradient accumulation steps.'}      )
						_lowerCAmelCase  : Optional[bool]						  =					field(
						    default=snake_case_     ,					metadata={'help': 'Use gradient checkpointing to reduce memory footprint.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(default=5_0_0_0_0     ,					metadata={'help': 'Maximum number of training steps.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(
						    default=-1     ,					metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(default=1_0_2_4     ,					metadata={'help': 'Sequence lengths used for training.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(default=1     ,					metadata={'help': 'Training seed.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(
						    default=1_0_2_4     ,					metadata={'help': 'Interval to save checkpoints. Measured as number of forward passes not training steps.'}     ,					)
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default=snake_case_     ,					metadata={'help': 'States path if the training should continue from a checkpoint folder.'}      )
						_lowerCAmelCase  : Optional[bool]						  =					field(default=snake_case_     ,					metadata={'help': 'If True the data is pretokenized.'}      )


@dataclass
class 						lowerCamelCase_				:
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='codeparrot/codeparrot'     ,					metadata={'help': 'Model name or path of model to be evaluated.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='codeparrot/codeparrot-clean-valid'     ,					metadata={'help': 'Name or path of validation dataset.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(default=2     ,					metadata={'help': 'Batch size used for evaluation.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(
						    default=-1     ,					metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(default=1_0_2_4     ,					metadata={'help': 'Length of sequences to be evaluated.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(default=1     ,					metadata={'help': 'Random seed used for evaluation.'}      )


@dataclass
class 						lowerCamelCase_				:
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='codeparrot/codeparrot'     ,					metadata={'help': 'Model name or path of model to be evaluated.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(default=snake_case_     ,					metadata={'help': 'Number of workers used for code evaluation.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(
						    default=snake_case_     ,					metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'}     ,					)
						_lowerCAmelCase  : Optional[bool]						  =					field(
						    default=snake_case_     ,					metadata={'help': 'Sample from the language model\'s output distribution.'}      )
						_lowerCAmelCase  : Optional[float]						  =					field(default=0.2     ,					metadata={'help': 'Sampling temperature used for generation.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(default=2_5_6     ,					metadata={'help': 'Maximum number of newly generated tokens.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(default=0     ,					metadata={'help': 'Top-k parameter used for generation.'}      )
						_lowerCAmelCase  : Optional[float]						  =					field(default=0.95     ,					metadata={'help': 'Top-p parameter used for nucleus sampling.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(default=1_0     ,					metadata={'help': 'Number of generations to run in parallel.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(
						    default=2_0_0     ,					metadata={'help': 'Number of completions to generate for each sample.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(default=1     ,					metadata={'help': 'Random seed used for evaluation.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='eval_results.json'     ,					metadata={'help': 'Random seed used for evaluation.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='0'     ,					metadata={'help': 'Allow `code_eval` to execute Python code on machine'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(
						    default=-1     ,					metadata={
						        'help': (
						            'Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive'
						            ' number corresponds to which GPU device id to run on.'
						        )
						    }     ,					)


@dataclass
class 						lowerCamelCase_				:
						_lowerCAmelCase  : Optional[int]						  =					field(
						    default=snake_case_     ,					metadata={
						        'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
						    }     ,					)
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='transformersbook/codeparrot'     ,					metadata={'help': 'Folder or name of dataset to process.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='codeparrot-clean'     ,					metadata={'help': 'Folder to save processed processed dataset.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(
						    default=1_0_0_0_0_0     ,					metadata={'help': 'Number of files to save per JSON output file.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(default='content'     ,					metadata={'help': 'Column containing text data to process.'}      )
						_lowerCAmelCase  : Optional[float]						  =					field(
						    default=1_0_0_0     ,					metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}      )
						_lowerCAmelCase  : Optional[float]						  =					field(
						    default=1_0_0     ,					metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'}      )
						_lowerCAmelCase  : Optional[float]						  =					field(
						    default=0.25     ,					metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'}      )
						_lowerCAmelCase  : Optional[float]						  =					field(
						    default=1.5     ,					metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'}      )
						_lowerCAmelCase  : Optional[float]						  =					field(
						    default=0.7     ,					metadata={'help': 'Probability for filtering config, test and uncommon files.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='codeparrot/codeparrot'     ,					metadata={'help': 'Name or path to the tokenizer.'}     ,					)
						_lowerCAmelCase  : Optional[bool]						  =					field(
						    default=snake_case_     ,					metadata={'help': 'If True, near-duplicate samples are removed.'}      )
						_lowerCAmelCase  : Optional[float]						  =					field(
						    default=0.85     ,					metadata={'help': 'Jaccard threshold for near-duplicate samples.'}      )


@dataclass
class 						lowerCamelCase_				:
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='gpt2'     ,					metadata={'help': 'Base tokenizer to build new tokenizer from.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='transformersbook/codeparrot-train'     ,					metadata={'help': 'Dataset to train tokenizer on.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(default='content'     ,					metadata={'help': 'Column containing text data to process.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(default=2_0_0_0_0_0     ,					metadata={'help': 'Number of examples to train tokenizer on.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(
						    default=3_2_7_6_8     ,					metadata={'help': 'Number of examples to train the tokenizer on.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(default='codeparrot'     ,					metadata={'help': 'Name of new tokenizer.'}      )
						_lowerCAmelCase  : Optional[bool]						  =					field(default=snake_case_     ,					metadata={'help': 'Push saved tokenizer to the hub.'}      )


@dataclass
class 						lowerCamelCase_				:
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='codeparrot/codeparrot'     ,					metadata={'help': 'Name or path to the tokenizer.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='codeparrot/codeparrot-clean-train'     ,					metadata={'help': 'Name or path to the dataset to pretokenize.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='tokenized-codeparrot-train'     ,					metadata={'help': 'Repo name of the pretokenized data.'}      )
						_lowerCAmelCase  : Optional[int]						  =					field(default=snake_case_     ,					metadata={'help': 'Number of workers used for code evaluation.'}      )


@dataclass
class 						lowerCamelCase_				:
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='gpt2-large'     ,					metadata={'help': 'Configuration to use for model initialization.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='codeparrot/codeparrot'     ,					metadata={'help': 'Tokenizer attached to model.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(default='codeparrot'     ,					metadata={'help': 'Name of the created model.'}      )
						_lowerCAmelCase  : Optional[bool]						  =					field(default=snake_case_     ,					metadata={'help': 'Push saved tokenizer to the hub.'}      )









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase_  :							Dict             =    {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_  :							List[Any]             =    [
							    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'EncodecModel',
							    'EncodecPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_encodec import (
							    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    EncodecConfig,
							)
							from .feature_extraction_encodec import EncodecFeatureExtractor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_encodec import (
														    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EncodecModel,
														    EncodecPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase_  :							List[str]             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




